3D cephalometric landmark detection by multiple stage deep reinforcement learning

Abstract The lengthy time needed for manual landmarking has delayed the widespread adoption of three-dimensional (3D) cephalometry. We here propose an automatic 3D cephalometric annotation system based on multi-stage deep reinforcement learning (DRL) and volume-rendered imaging. This system consider...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Sung Ho Kang, Kiwan Jeon, Sang-Hoon Kang, Sang-Hwy Lee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/14c3583b99004793a05cc73915f23c71
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:14c3583b99004793a05cc73915f23c71
record_format	dspace
spelling	oai:doaj.org-article:14c3583b99004793a05cc73915f23c712021-12-02T15:28:57Z3D cephalometric landmark detection by multiple stage deep reinforcement learning10.1038/s41598-021-97116-72045-2322https://doaj.org/article/14c3583b99004793a05cc73915f23c712021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97116-7https://doaj.org/toc/2045-2322Abstract The lengthy time needed for manual landmarking has delayed the widespread adoption of three-dimensional (3D) cephalometry. We here propose an automatic 3D cephalometric annotation system based on multi-stage deep reinforcement learning (DRL) and volume-rendered imaging. This system considers geometrical characteristics of landmarks and simulates the sequential decision process underlying human professional landmarking patterns. It consists mainly of constructing an appropriate two-dimensional cutaway or 3D model view, then implementing single-stage DRL with gradient-based boundary estimation or multi-stage DRL to dictate the 3D coordinates of target landmarks. This system clearly shows sufficient detection accuracy and stability for direct clinical applications, with a low level of detection error and low inter-individual variation (1.96 ± 0.78 mm). Our system, moreover, requires no additional steps of segmentation and 3D mesh-object construction for landmark detection. We believe these system features will enable fast-track cephalometric analysis and planning and expect it to achieve greater accuracy as larger CT datasets become available for training and testing.Sung Ho KangKiwan JeonSang-Hoon KangSang-Hwy LeeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sung Ho Kang Kiwan Jeon Sang-Hoon Kang Sang-Hwy Lee 3D cephalometric landmark detection by multiple stage deep reinforcement learning
description	Abstract The lengthy time needed for manual landmarking has delayed the widespread adoption of three-dimensional (3D) cephalometry. We here propose an automatic 3D cephalometric annotation system based on multi-stage deep reinforcement learning (DRL) and volume-rendered imaging. This system considers geometrical characteristics of landmarks and simulates the sequential decision process underlying human professional landmarking patterns. It consists mainly of constructing an appropriate two-dimensional cutaway or 3D model view, then implementing single-stage DRL with gradient-based boundary estimation or multi-stage DRL to dictate the 3D coordinates of target landmarks. This system clearly shows sufficient detection accuracy and stability for direct clinical applications, with a low level of detection error and low inter-individual variation (1.96 ± 0.78 mm). Our system, moreover, requires no additional steps of segmentation and 3D mesh-object construction for landmark detection. We believe these system features will enable fast-track cephalometric analysis and planning and expect it to achieve greater accuracy as larger CT datasets become available for training and testing.
format	article
author	Sung Ho Kang Kiwan Jeon Sang-Hoon Kang Sang-Hwy Lee
author_facet	Sung Ho Kang Kiwan Jeon Sang-Hoon Kang Sang-Hwy Lee
author_sort	Sung Ho Kang
title	3D cephalometric landmark detection by multiple stage deep reinforcement learning
title_short	3D cephalometric landmark detection by multiple stage deep reinforcement learning
title_full	3D cephalometric landmark detection by multiple stage deep reinforcement learning
title_fullStr	3D cephalometric landmark detection by multiple stage deep reinforcement learning
title_full_unstemmed	3D cephalometric landmark detection by multiple stage deep reinforcement learning
title_sort	3d cephalometric landmark detection by multiple stage deep reinforcement learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/14c3583b99004793a05cc73915f23c71
work_keys_str_mv	AT sunghokang 3dcephalometriclandmarkdetectionbymultiplestagedeepreinforcementlearning AT kiwanjeon 3dcephalometriclandmarkdetectionbymultiplestagedeepreinforcementlearning AT sanghoonkang 3dcephalometriclandmarkdetectionbymultiplestagedeepreinforcementlearning AT sanghwylee 3dcephalometriclandmarkdetectionbymultiplestagedeepreinforcementlearning
_version_	1718387187165167616

3D cephalometric landmark detection by multiple stage deep reinforcement learning

Ejemplares similares